⚠️ 运行脚本: 所有 node scripts/... 命令必须以本 SKILL.md 所在目录为 workdir。 AiCoin Freqtrade Freqtrade strategy creation, backtesting, and deployment powered by AiCoin Open API . Critical Rules ALWAYS use ft-deploy.mjs backtest for backtesting. NEVER write custom backtest scripts. NEVER use simulated/fabricated data. ALWAYS use ft-deploy.mjs deploy for deployment. NEVER use Docker. NEVER manually run freqtrade commands. NEVER manually edit Freqtrade config files. Use ft-deploy.mjs actions. NEVER manually run freqtrade trade , source .venv/bin/activate , or pip install freqtrade . ACTIVELY use AiCoin data in strategies. Check what data the user's API key supports and integrate it. Don't only use basic indicators when richer data is available. Two Ways to Create Strategies Option A: Quick Generator (for simple strategies) create_strategy generates a ready-to-backtest strategy file with selected indicators and optional AiCoin data: node scripts/ft-deploy.mjs create_strategy '{"name":"MACDStrategy","timeframe":"15m","indicators":["macd","rsi","atr"]}' node scripts/ft-deploy.mjs create_strategy '{"name":"WhaleStrat","timeframe":"15m","indicators":["rsi","macd"],"aicoin_data":["funding_rate","ls_ratio"]}' Available indicators : rsi , bb , ema , sma , macd , stochastic / kdj , atr , adx , cci , williams_r , vwap , ichimoku , volume_sma , obv Option B: Write Custom Strategy Code (for complex/custom strategies) When users need custom logic beyond what create_strategy offers, write a Python strategy file directly. Use the AiCoin Python SDK ( aicoin_data.py , auto-installed at ~/.freqtrade/user_data/strategies/ ) to integrate real market data. Strategy file location: ~/.freqtrade/user_data/strategies/YourStrategyName.py AiCoin Python SDK Reference from aicoin_data import AiCoinData , ccxt_to_aicoin ac = AiCoinData ( cache_ttl = 300 )

Auto-loads API keys from .env

Convert CCXT pair to AiCoin symbol

symbol

ccxt_to_aicoin ( "BTC/USDT:USDT" , "binance" )

→ "btcswapusdt:binance"

── Free tier (no key needed) ──

ac . coin_ticker ( "bitcoin" )

Real-time price

ac . kline ( symbol , period = "3600" )

K-line data (period in seconds)

ac . hot_coins ( "market" )

── 基础版 ($29/mo) ──

ac . funding_rate ( symbol )

Funding rate history

ac . funding_rate ( symbol , weighted = True )

Volume-weighted cross-exchange rate

ac . ls_ratio ( )

Aggregated long/short ratio

── 标准版 ($79/mo) ──

ac . big_orders ( symbol )

Whale/large orders

ac . agg_trades ( symbol )

Aggregated large trades

── 高级版 ($299/mo) ──

ac . liquidation_map ( symbol , cycle = "24h" )

Liquidation heatmap

ac . liquidation_history ( symbol )

Liquidation history

── 专业版 ($699/mo) ──

ac . open_interest ( "BTC" , interval = "15m" )

Aggregated open interest

ac . ai_analysis ( [ "BTC" ] )

AI-powered analysis

Complete Strategy Template (copy and customize)

MyCustomStrategy - Description

Uses AiCoin data in live/dry_run mode

from freqtrade . strategy import IStrategy , IntParameter , DecimalParameter from pandas import DataFrame import logging , time logger = logging . getLogger ( name ) class MyCustomStrategy ( IStrategy ) : INTERFACE_VERSION = 3 timeframe = '15m' can_short = True minimal_roi = { "0" : 0.05 , "60" : 0.03 , "120" : 0.01 } stoploss = - 0.05 trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03

Hyperopt parameters

rsi_buy

IntParameter ( 20 , 40 , default = 30 , space = 'buy' ) rsi_sell = IntParameter ( 60 , 80 , default = 70 , space = 'sell' )

AiCoin data cache

_ac_funding_rate

0.0 _ac_ls_ratio = 0.5 _ac_whale_signal = 0.0 _ac_last_update = 0.0 def populate_indicators ( self , dataframe : DataFrame , metadata : dict ) -

DataFrame :

=== Technical Indicators (always available) ===

RSI

delta

dataframe [ 'close' ] . diff ( ) gain = delta . clip ( lower = 0 ) . rolling ( window = 14 ) . mean ( ) loss = ( - delta . clip ( upper = 0 ) ) . rolling ( window = 14 ) . mean ( ) rs = gain / loss dataframe [ 'rsi' ] = 100 - ( 100 / ( 1 + rs ) )

MACD

ema12

dataframe [ 'close' ] . ewm ( span = 12 , adjust = False ) . mean ( ) ema26 = dataframe [ 'close' ] . ewm ( span = 26 , adjust = False ) . mean ( ) dataframe [ 'macd' ] = ema12 - ema26 dataframe [ 'macd_signal' ] = dataframe [ 'macd' ] . ewm ( span = 9 , adjust = False ) . mean ( )

EMA

dataframe [ 'ema_fast' ] = dataframe [ 'close' ] . ewm ( span = 8 , adjust = False ) . mean ( ) dataframe [ 'ema_slow' ] = dataframe [ 'close' ] . ewm ( span = 21 , adjust = False ) . mean ( )

=== AiCoin Data (live/dry_run only) ===

dataframe [ 'funding_rate' ] = 0.0 dataframe [ 'ls_ratio' ] = 0.5 dataframe [ 'whale_signal' ] = 0.0 if self . dp and self . dp . runmode . value in ( 'live' , 'dry_run' ) : now = time . time ( ) if now - self . _ac_last_update

300 :

Update every 5 min

self . _update_aicoin_data ( metadata ) self . _ac_last_update = now

Apply latest AiCoin data to current candle

dataframe . iloc [ - 1 , dataframe . columns . get_loc ( 'funding_rate' ) ] = self . _ac_funding_rate dataframe . iloc [ - 1 , dataframe . columns . get_loc ( 'ls_ratio' ) ] = self . _ac_ls_ratio dataframe . iloc [ - 1 , dataframe . columns . get_loc ( 'whale_signal' ) ] = self . _ac_whale_signal return dataframe def _update_aicoin_data ( self , metadata : dict ) : """Fetch latest AiCoin data. Called every 5 min in live mode.""" try : import sys , os _sd = os . path . dirname ( os . path . abspath ( file ) ) if _sd not in sys . path : sys . path . insert ( 0 , _sd ) from aicoin_data import AiCoinData , ccxt_to_aicoin ac = AiCoinData ( cache_ttl = 300 ) pair = metadata . get ( 'pair' , 'BTC/USDT:USDT' ) exchange = self . config . get ( 'exchange' , { } ) . get ( 'name' , 'binance' ) symbol = ccxt_to_aicoin ( pair , exchange )

Funding rate (基础版)

try : data = ac . funding_rate ( symbol , weighted = True , limit = '5' ) items = data . get ( 'data' , [ ] ) if isinstance ( items , list ) and items : latest = items [ 0 ] if isinstance ( latest , dict ) and 'close' in latest : self . _ac_funding_rate = float ( latest [ 'close' ] ) * 100 except Exception as e : logger . debug ( f"AiCoin funding_rate unavailable: { e } " )

Long/short ratio (基础版)

try : ls = ac . ls_ratio ( ) detail = ls . get ( 'data' , { } ) . get ( 'detail' , { } ) if detail : ratio = float ( detail . get ( 'last' , 1.0 ) ) self . _ac_ls_ratio = max ( 0.0 , min ( 1.0 , ratio / ( 1.0 + ratio ) ) ) except Exception as e : logger . debug ( f"AiCoin ls_ratio unavailable: { e } " )

Whale orders (标准版)

try : orders = ac . big_orders ( symbol ) if 'data' in orders and isinstance ( orders [ 'data' ] , list ) : buy_vol = sum ( float ( o . get ( 'amount' , 0 ) ) for o in orders [ 'data' ] if o . get ( 'side' , '' ) . lower ( ) in ( 'buy' , 'bid' , 'long' ) ) sell_vol = sum ( float ( o . get ( 'amount' , 0 ) ) for o in orders [ 'data' ] if o . get ( 'side' , '' ) . lower ( ) in ( 'sell' , 'ask' , 'short' ) ) total = buy_vol + sell_vol if total

0 : self . _ac_whale_signal = ( buy_vol - sell_vol ) / total except Exception as e : logger . debug ( f"AiCoin big_orders unavailable: { e } " ) except ImportError : logger . warning ( "aicoin_data module not found. Run ft-deploy.mjs deploy to install." ) except Exception as e : logger . warning ( f"AiCoin data error: { e } " ) def populate_entry_trend ( self , dataframe : DataFrame , metadata : dict ) -

DataFrame :

Long: RSI oversold + MACD bullish + AiCoin confirmations

dataframe . loc [ ( dataframe [ 'rsi' ] < self . rsi_buy . value ) & ( dataframe [ 'macd' ]

dataframe [ 'macd_signal' ] ) & ( dataframe [ 'ema_fast' ]

dataframe [ 'ema_slow' ] ) & ( dataframe [ 'ls_ratio' ] <= 0.55 ) &

More shorts = contrarian long

( dataframe [ 'whale_signal' ]

=

0.3 ) &

Whales not heavily selling

( dataframe [ 'volume' ]

0 ) , 'enter_long' ] = 1

Short: RSI overbought + MACD bearish + AiCoin confirmations

dataframe . loc [ ( dataframe [ 'rsi' ]

self . rsi_sell . value ) & ( dataframe [ 'macd' ] < dataframe [ 'macd_signal' ] ) & ( dataframe [ 'ema_fast' ] < dataframe [ 'ema_slow' ] ) & ( dataframe [ 'ls_ratio' ] = 0.45 ) &

More longs = contrarian short

( dataframe [ 'whale_signal' ] <= 0.3 ) &

Whales not heavily buying